In conclusion, complexnetwork analysis conducted in this study yielded a broad and more detailed view of genomic and molecular mechanisms involved in RMTLE in comparison to analyses centered on differentially expressed genes. Specifically, hubs and VIPs in DE networks are mostly related to neuronal excitability and, in a broad sense, play pro-epileptic roles. Conversely, hubs, VIPs and high-hubs in CO networks are more frequently related to neuronal differentiation, neuroprotection and synaptic function, in a scenario compatible with compensatory mechanisms that may play a reparatory role, but may also be linked to epilepsy pathogenesis [138,139]. The DE genes with higher connectivity occupy a central position in both DE and CO networks, reflecting their biological essentiality and role in disease . The same network centrality is observed for the hubs, VIPs and high-hubs of CO networks, being consistent with the network disease model , where a group of nodes whose perturbation leads to a disease phenotype forms a disease module occupying a central network position [21,140]. This finding indicates that the probability of exerting therapeutic effects through the modulation of particular genes will be higher if these genes are highly interconnected in transcriptional networks .
We use model complex networks to testify our approach by employing PDG on three types of complex networks: random, small-world, and scale-free. During the system’s evolution to- wards the steady state, we record time series of strategies and payoffs to reveal the topology of the interaction network. We implement the success rates of existent links (SREL) and nonexis- tent links (SRNL) to quantify the performance of the amount of required measurements. If the predicted value of an element of the adjacency matrix A approaches to zero, we regard there is no connection, otherwise, there exists corresponding connection if the value approaches 1. In reality, we arrange the threshold smaller, e.g., 0.1, then the range of existent links is 1 ± 0.1 and the range of nonexistent links is 0 ± 0.1. Any value beyond the two sections is supposed as a failure of the prediction. For a single player, SERL is defined as the ratio of successfully pre- dicted neighbor-connection links to that of actual neighbors and SRNL is defined in a similar way. Then we acquire the values of SREL and SRNL by averaging all nodes for the whole net- work. Due to the sparsity of the potential complexnetwork where the number of nonexistent links is usually larger than that of existent links, we treat SREL and SRNL separately. Under the circumstance that the chosen threshold is neither too close to 1, nor to zero, it slightly affects the overall success rates.
This paper explores the relationships between international human migration and merchan- dise trade using a complex-network approach. We firstly compare the topological structure of worldwide networks of human migration and bilateral trade over the period 1960–2000. Next, we ask whether pairs of countries that are more central in the migration network trade more. We show that: (i) the networks of international migration and trade are strongly corre- lated, and such correlation can be mostly explained by country economic/demographic size and geographical distance; (ii) centrality in the international-migration network boosts bilat- eral trade; (iii) intensive forms of country centrality are more trade enhancing than their ex- tensive counterparts. Our findings suggest that bilateral trade between any two countries is not only affected by the presence of migrants from either countries, but also by their relative embeddedness in the complex web of corridors making up the network of international human migration.
Recently, network analyses have been applied to study evolutionary relationships, showing that these analyses are suitable to advance the understanding of genomic evolution [16, 17]. In this work, we investigate the origins of mitochondria through a complexnetwork approach focused on modularity (or community structure) analysis that has been recently used in a fruit- ful manner to study fungal protein networks [18, 19]. A set of complex networks is constructed by taking into account an index of structural similarity S between equivalent proteins in differ- ent organisms. In this framework, the network nodes correspond to distinct organisms that are represented by their proteins. The presence of network connections is related to the protein similarity, which is also an indication of a relationship between the organisms that synthesize those proteins. The main task is the identification of network communities, which reflects the way the organisms are grouped together, and hint at how mitochondria may have evolved from their ancestors. The results are based on data from protein sequences of three subunits from the F 0 portion (4, 6, and 9) of the ATP synthase complex and their homologs in the
Both the Vanunu and Li methods mentioned above did not make use of protein complexes to aid in their inference of gene- phenotype associations. In an earlier work, Lage et al.  made use of protein complexes for prioritization of disease genes via phenotypic weighting of protein complexes linked to human diseases. However, they did not use actual protein complexes but simply assembled neighboring proteins as complexes (consist of a protein and all their direct interaction partners). They also ignored the biological relationships between the protein complexes. For example, it has been reported that if two protein complexes share a number of common proteins or have densely physical interactions between them, the mutations of genes in one protein complex could lead to same or similar phenotypes of the other protein complex . As such, incorporating quality-controlled protein complexes and accounting for their relationships are both essential for accurate disease gene prediction. In this work, we therefore propose to construct a novel protein complexnetwork, where nodes are individual complexes and the interactions between two complexes are measured by the connection strengths between them, as a basis for interrogating the phenome- interactome networks for disease gene prioritization. We devise a
Recently, the number of studies involving complexnetwork applications in transportation has increased steadily as scholars from various fields analyze traffic networks. Nonethe- less, research on rail network growth is relatively rare. This research examines the evolution of the Public Urban Rail Transit Networks of Kuala Lumpur (PURTNoKL) based on complexnetwork theory and covers both the topological structure of the rail system and future trends in network growth. In addition, network performance when facing different attack strategies is also assessed. Three topological network characteristics are considered: connections, clustering and centrality. In PURTNoKL, we found that the total number of nodes and edges exhibit a linear relationship and that the average degree stays within the interval [2.0488, 2.6774] with heavy-tailed distributions. The evolutionary process shows that the cumulative probability distribution (CPD) of degree and the average shortest path length show good fit with exponential distribution and normal distribution, respectively. Moreover, PURTNoKL exhibits clear cluster characteristics; most of the nodes have a 2-core value, and the CPDs of the centrality’s closeness and betweenness follow a normal distribution function and an exponential distribution, respectively. Finally, we discuss four different types of network growth styles and the line extension process, which reveal that the rail network’s growth is likely based on the nodes with the biggest lengths of the shortest path and that network pro- tection should emphasize those nodes with the largest degrees and the highest between- ness values. This research may enhance the networkability of the rail system and better shape the future growth of public rail networks.
, and the underground transportation in Boston and Vienna [13, 14]. While some of the studies have focused on the dynamic properties of complexnetwork, most of the works in the literature of complex networks have focused on the characterization of topological properties such as the small world behavior and scale free structure . In this paper, we study the topological properties of the Guangzhou Metro. The network, which transfixes all directions with 236 kilometers of tracks, has been formed in Guangzhou, with an average passenger capacity of 3,240,000 passenger trips per day. Having delivered 1.85 billion rides in 2012, Guangzhou Metro is the sixth busiest metro system in the world, after the metro systems of Tokyo, Seoul, Moscow, Beijing, and Shanghai .The rest of the paper is laid out as follows. In section 2 we discuss the network construction and the topological properties of the network whereas section 3 concludes the paper.
In traditional literature, the time evolution of biochemical reacting system is often treated as a continuous and deterministic process [21, 22]. However this approach should not always be taken for granted. More and more studies [23-25] have accepted the fact that the time evolution of a spatially homogeneous biochemical system is a discrete, stochastic pro- cess instead of a continuous, deterministic process. In this section, the stochastic biochemical reaction model (SBRM) is proposed to analyze the dynamics of biochemical network. For thermal equilibrium, the concentration of species is the most important index in a fixed vol- ume. The traditional way of defining concentration is that the amount of biochemical species n is variable while the volume v is fixed. This definition is not feasible in view of complexnetwork.
from the data because much of the variance in streamflow is associated with seasonality. Use of Spearman correlation has a tendency to increase the number of links between sta- tions because rank correlation allows for more complex (yet monotonic) relationships. However, these choices do not af- fect the global network structure as diagnosed by the cluster- ing coefficient or average path length. Note also that when making the decision to use absolute or anomalous values, we may additionally refer back to one of the major impe- tuses for this paper, which is to use network theory to as- sess how well the current array of streamflow gauges sam- ples the hydrology of the Coast Mountains and to explore how network theoretic insights might help guide future deci- sions on streamflow monitoring system design. That is, the emphasis lies on actual river flows, as might be required for water supply, ecology, civil engineering, or other potential applications. These actual discharge values are influenced to a considerable degree by seasonal forcing, and therefore re- quire direct sampling by a hydrometric monitoring system. Additionally, sharing a common seasonal flow regime, espe- cially within our study region (where seasonal regimes ex- hibit great basin-to-basin heterogeneity as discussed in detail above), is a fundamentally meaningful and operationally im- portant physical link between two stations. That is, we would in general wish the network analysis, and a streamflow mon- itoring system, to directly capture such connections. Further discussion on the use of anomalous values of geophysical data and network analysis can be found in Tsonis and Roeb- ber (2004).
A crucial point raised is which factors help determine between antagonism and cooperation within a given cell? The answer to this question is very complex and to date not yet elucidated. One important consideration is the expression level of each transcrip- tion factor which depends on the specific cell type. For instance, it has been reported that the LIP/LAP ratio may dictate the C/ EBPb transcriptional activity and depend on the cell type . In addition, while most proteins including YY1 and USF are ubiquitously expressed [35,36], we must keep in mind that enforced expression of transcription factors may not reflect their real endogenous activities. To gain insight into how such a coordination of gene expression might occur, it will be also instructive to examine posttranslational modifications. Indeed, mounting evidence suggests that acetylation and phosphorylation of nuclear factors may be interdependent . For instance, C/ EBPb recruits p300, triggers massive phosphorylation within its C- terminal domain and thereby modulates p300 activity as a co- activator . Recently, other investigators revealed a cooperation between acetylation and phosphorylation in the control of GATA- 1 transcription factor activity . In this regard, an interesting point deserving further attention is the evaluation of both C/EBPb phosphorylation and acetylation status including mutations of the target sites and their impact on C/EBPb DNA binding and protein-protein interaction abilities.
Concluding, our approach shows that the complex climate networks approach yields meaningful climate information and has the potential to improve skill measures within the framework of climate prediction. It is the first time that such network techniques have been used in climate predictions. Since climate or decadal predictions aim to predict natural variability on the order of years, suitable statistics are needed. Natural variability on the order of years evolves highly dy- namically and often nonlinearly. Thus, the complex climate networks could bear the potential to be very useful in climate predictions. Our approach, which is even based on the most simple network measure, the node degree (or as we used it the link strength), yields optimistic results. So, we think that our analysis could be the starting point for using the com- plex networks in climate predictions. Using other measures and/or multivariate data could turn out to be the better way of analyzing predictions of natural variability years ahead than using methods from short- or medium-range forecast- ing. Further, from the network perspective it would be in- teresting to analyze other network measures like clustering, similarities, or path lengths and how they are connected to climate evolution. The incorporation of other relevant vari- ables like precipitation, wind, or soil moisture into the net- work is an appealing aspect. From a physical or climatolog- ical point of view it is important to understand why the net- work measures are able to represent climate dynamics, which
weather situations and hence to a complex climate (e.g. CORDEX-EU, Jacob et al., 2013; Giorgi et al., 2009). Nevertheless, the European continent is influenced by the AMOC and thus this process may yield to a certain predictability, although the signal to noise ratio is most probably small. Up to now, the prediction skill for Europe is weaker than for such regions as the South Pacific or North Atlantic. Mieruch et al.
yield a different result. The degree distribution of the USF association network follows not a pure power law, but a truncated power law with an initially exponential decay. Indeed, a closer look at Steyvers and Tenenbaum’s  plotted distribution reveals that it deviates from a pure power law. This difference in the scale-free structure of the association network may be the pri- mary reason for the incompatibility between their arguments and ours regarding the ability of the DSM. Furthermore, in our analysis, the degree distribution of the LSA network (which cor- responds to the DSM network created from the tf-idf-weighted and smoothed word-document matrix) also exhibits a similar degree distribution, although its power-law slope is steeper than that of the association network. More importantly, some semantic spaces generated by DSM methods other than LSA (e.g., semantic spaces generated from the weighted and unsmoothed word-word matrices) yield a degree distribution very similar to that of the association network. Steyvers and Tenenbaum  analyzed only the semantic network based on LSA, and specu- lated that other semantic spaces are unlikely to reproduce scale-free connectivity. This specula- tion is derived from the assumption that LSA and other semantic spaces share geometric properties of Euclidean-space semantic representations that are not consistent with human similarity judgments. Our findings indicate that this speculation is not valid. Hence, it provides empirical evidence of the DSM’s ability to simulate the network structure underlying human semantic knowledge or word association; this is one of the original contributions of this study.
Abstract—This work proposes a genetic algorithm for design- ing a wireless sensor network based on complexnetwork theory. We develop an heuristic approach based on genetic algorithms for finding a network configuration such that its communication structure presents complexnetwork characteristics, e.g. a small value for the average shortest path length and high cluster coefficient. The work begins with the mathematical model of the hub location problem, developed to determine the nodes which will be configured as hubs. This model was adopted within the genetic algorithm. The results reveal that our methodology allows the configuration of networks with more than a hundred nodes with complexnetwork characteristics, thus reducing the energy consumption and the data transmission delay.
A relatively complexnetwork of reactions has been investigated, using as a network model the isothermal batch esterification of acetic acid with ethanol in n-heptane catalyzed by lyophilized mycelium of Aspergillus oryzae. The kinetic analysis was firstly carried out on the whole system, without any simplification, by means of the well-known integral method. Owing to the poor results obtained by this way, we developed an alternative approach, combining initial rates and integral analysis and reducing the number of empirical parameters to be determined by the use of equilibrium data. All the values of the parameters calculated according to this “composite” approach to kinetic analysis well correlate with experimental data.
The complexnetwork topologies which result from the self-organizing processes, based on biological mechanisms, which we have proposed, have properties that are common features of many biological systems and indicate that they may not be well described by BG statistical mechanics, but rather by Nonextensive Statistical Mechanics. These mecha- nisms are very characteristic of much of the brain’s function- ing and suggest the use of a GSA algorithm to model memory functioning and the way we associate ideas in thought. The study of network quantities such as node degree distributions and clustering coefficients may indicate possible experiments, which would validate models such as the one presented here. We are proceeding in further model refinement and analy- sis. It is necessary to verify more thoroughly the dependence of model behavior on its various parameters such as T and σ. These parameters represent, at least partially, the effects of neurosubstances such as neural growth factors, neurotrans- mitters and neuromodulators. Although we do not have ex- perimental indications of their absolute values, the tuning of their relative values is fundamental for model stability and functioning. This may give some insight to basic mechanisms in real neural networks and the emergence of behavioral as- pects. We are also interested in trying to map language struc- ture and processing into network topology and dynamics, al- though we are not yet sure whether this is possible.
In this paper we analyse a complexnetwork of international shipping routes using a first-order Markov chain model to identify potential origins of introduction (i.e. source countries and international foreign ports) for the highly invasive Khapra beetle (Trogoderma granarium) to Australia. We also identify and rank those Australian ports most likely to receive this invasive species. This analysis was undertaken with a probabilistic pathway model that describes the likelihood of the pest being moved from port i to port j as a linear function of the number of trips made by container ships through the segment ij and depicted as a matrix of the transmission probabilities p ij . This matrix is then used to simulate
Consequently, the heuristic search algorithm adds arcs only in the forward direction because this protects the network from having cycles and complexnetwork structure. On the other hand, there is a price of arranging the variables at the creation of the database in the heuristic algorithm. Since the agents will not have much knowledge about the environmental variables, it is hard to arrange the variables at the beginning. There is a need for a better search algorithm that explores more possibilities in the network. The following paragraph introduces another searching algorithm that eliminates the arranging the variables, namely exhaustive search.
A water distribution system (WDS) is a complexnetwork of interconnected pipes that delivers water from the source(s) to consumers. In addition to the pipes, which are major components, a WDS involves mechanical and hydraulic control elements such as pumps, storage tanks, reservoirs, regulators, valves and joints (Gheisi & Naser, 2013). Therefore, there are different types of leaks, including service line leaks and valve leaks. However, in most cases, the largest portion of water that is unaccounted for is lost through leaks in supply lines. There are many possible causes of leaks, and often a combination of factors leads to their incidence. The factors that may contribute to leaks in pipes include the following: the material, composition, age, and joining methods of the distribution system components; corrosion and deterioration of pipes; elevated pressures in the water network; soil characteristics and movement. Water conditions, including the temperature, velocity, and pressure, are also a factor (Arreguín-Cortes & Ochoa-Alejo, 1997).